Social networks are generally made up of individuals (i.e., “nodes”) connected by one or more specific types of connections (i.e., “ties”). Commercially available social network platforms, such as the FACEBOOK™ platform, allow users to connect to each other over the internet by “friending” each other (i.e., creating a “friend” connection between users). Some social network platforms have expanded to represent objects in addition to individuals and the connections between individuals and those objects. For example the FACEBOOK™ platform has expanded to include objects (e.g., users, photos, and webpages), and the connections between them (e.g., friendships, photo tags, and likes). In addition to allowing users to easily stay in touch with old friends and meet new friends through connections from existing friends, the connections involved in social networks may be useful for inferring interests of a user.
A social graph is a concept that describes the relationship between online users defined explicitly by the connections between the users. For example, the FACEBOOK™ graph Application Program Interface (“API”) allows third parties to access objects in the graph and the connections associated with objects. It has long been known that graph analysis can lead to insights. Social graphs may be mined, for example by collaborative filtering, to infer user preferences.
However, social graphs keep minimal data about the connections themselves. For example, the FACEBOOK™ platform provides that a connection between users may be a “friend” connection and that a connection between a user and a product may be a “like” connection; however connections other than those specifically defined by the platform must be inferred. This may lead to misguided inferences because the lack of data about the connection (i.e., connection metadata).
For example, mere knowledge of the fact that users “like” plural products may lead to misguided inferences without structured information relating the connections or to the plural products. As a simple example, a first user may like a PLAYSTATION 3™ and a second user, who is a friend of the first user, may like an LG™ Blu-ray player. Conventional methods of inferring user preferences may infer that the first user would like the LG™ Blu-ray player because his friend does. However, because the PLAYSTATION 3™ has an integrated Blu-ray player, the first user may not like any stand-alone Blu-ray player and instead find standalone Blu-ray plays to be unnecessary peripherals.
While increasingly large data sets and increasingly sophisticated data mining techniques may help to improve the quality of inferences mined from graphs, the improvement comes at a great cost of data gathering and processing. Further, individual connections between objects in a graph may seem anomalous simply because of a lack of understanding of the connection.
While systems and methods are described herein by way of example and embodiments, those skilled in the art recognize that systems and methods for using social media data to form connections between assets are not limited to the embodiments or drawings described. It should be understood that the drawings and description are not intended to be limiting to the particular form disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the appended claims. Any headings used herein are for organizational purposes only and are not meant to limit the scope of the description or the claims. As used herein, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to.